Photovoltaic (PV) energy grows rapidly and is crucial for the decarbonization of electric systems. However, centralized registries recording the technical characteristics of rooftop PV systems are often missing, making it difficult to monitor this growth accurately. The lack of monitoring could threaten the integration of PV energy into the grid. To avoid this situation, remote sensing of rooftop PV systems using deep learning has emerged as a promising solution. However, existing techniques are not reliable enough to be used by public authorities or transmission system operators (TSOs) to construct up-to-date statistics on the rooftop PV fleet. The lack of reliability comes from deep learning models being sensitive to distribution shifts. This work comprehensively evaluates distribution shifts’ effects on the classification accuracy of deep learning models trained to detect rooftop PV panels on overhead imagery. We construct a benchmark to isolate the sources of distribution shifts and introduce a novel methodology that leverages explainable artificial intelligence (XAI) and decomposition of the input image and model’s decision regarding scales to understand how distribution shifts affect deep learning models. Finally, based on our analysis, we introduce a data augmentation technique designed to improve the robustness of deep learning classifiers under varying acquisition conditions. Our proposed approach outperforms competing methods and can close the gap with more demanding unsupervised domain adaptation methods. We discuss practical recommendations for mapping PV systems using overhead imagery and deep learning models.

The accumulation area ratio (AAR) of a glacier reflects its current state of equilibrium, or disequilibrium, with climate and its vulnerability to future climate change. Here, we present an inventory of glacier-specific annual accumulation areas and equilibrium line altitudes (ELAs) for over 3000 glaciers in Alaska and northwest Canada (88% of the regional glacier area) from 2018 to 2022 derived from Sentinel-2 imagery. We find that the 5 year average AAR of the entire study area is 0.41, with an inter-annual range of 0.25–0.49. More than 1000 glaciers, representing 8% of the investigated glacier area, were found to have effectively no accumulation area. Summer temperature and winter precipitation from ERA5-Land explained nearly 50% of the inter-annual ELA variability across the entire study region (${R}^2=0.47$). An analysis of future climate scenarios (SSP2-4.5) projects that ELAs will rise by ∼170 m on average by the end of the 21st century. Such changes would result in a loss of 25% of the modern accumulation area, leaving a total of 1900 glaciers (22% of the investigated area) with no accumulation area. These results highlight the current state of glacier disequilibrium with modern climate, as well as glacier vulnerability to projected future warming.

Water hyacinth is a highly invasive, aquatic species in the southern US that requires intensive management through frequent herbicide applications to minimize harmful impacts. Quantifying management success in large-scale operations is challenging with traditional survey methods, which rely on boat-based teams and can be time-consuming and labor-intensive. In contrast, unmanned aerial systems allow a single operator to survey a waterbody more efficiently and rapidly, enhancing both coverage and data collection. Therefore, the objective of this research was to develop remote sensing techniques to assess herbicide efficacy for water hyacinth control in an outdoor mesocosm study. Experiments were conducted in spring and summer 2023 to compare and correlate data from visual evaluations of herbicide efficacy against nine vegetation indices (VIs) derived from unmanned aerial system (UAS)-based red-green-blue (RGB) imagery. Penoxsulam, carfentrazone, diquat, 2,4-D, florpyrauxifen-benzyl, and glyphosate were applied at two rates, and experimental units were evaluated for six weeks. The Carotenoid Reflectance Index (CRI) had the highest Spearman’s correlation coefficient with visually evaluated efficacy for 2,4-D, diquat, and florpyrauxifen benzyl (> -0.77). The Visible Atmospherically Resistance Index (VARI) had the highest correlation for carfentrazone and penoxsulam treatments (> -0.70), and the EXGR Excess Greenness Minus Redness Index had the highest correlation for glyphosate treatments (> -0.83). CRI had the highest correlation coefficient with the most herbicide treatments, and it was the only VI tested that did not include the red band. These vegetation indices were satisfactory predictors of mid-range visually evaluated herbicide efficacy values but were poorly correlated with extremely low and high values, corresponding to non-treated and necrotic plants. Future research should focus on applying findings to real-world (non-experimental) field conditions and testing imagery with spectral bands beyond the visible range.

Recent observations have shown a fast decrease in thickness and area of Pyrenean glaciers in some cases leading to a stagnation of ice flow. However, their transition to a new paraglacial stage is not well understood. Through the combination of uncrewed aerial vehicles imagery, airborne LiDAR, ground-penetrating radar and ground temperature observations, we characterized the recent evolution of Infiernos Glacier. In 2021, this glacier had small sectors thicker than 25 m, but most of area did not exceed 10 m. The thickness losses from 2011 to 2023 reached 9 m in average, of which 5 m occurring during the period 2020–23. This trend demonstrates the significant ice melt under current climatic conditions. In the last years, the glacier has also shown a remarkable increase of debris cover extent. In these areas, the ice loss was reduced by half when compared to the thickness decrease in the entire glacier. Sub-freezing ground temperatures evidence the highly probable presence of permafrost or buried ice in the surroundings of the glacier. The clear signs of ice stagnation and the magnitude of area and thickness decrease support the main hypothesis of this work: After 2023, the Infiernos Glacier can no longer be considered a glacier and has become an ice patch.

The severe ice losses observed for European glaciers in recent years have increased the interest in monitoring short-term glacier changes. Here, we present a method for constraining modelled glacier mass balance at the sub-seasonal scale and apply it to ten selected glaciers in the Swiss Alps over the period 2015–23. The method relies on observations of the snow-covered area fraction (SCAF) retrieved from Sentinel-2 imagery and long-term mean glacier mass balances. The additional information provided by the SCAF observations is shown to improve winter mass balance estimates by 22% on average over the study sites and by up to 70% in individual cases. Our approach exhibits good performance, with a mean absolute deviation (MAD) to the observed seasonal mass balances of 0.28 m w.e. and an MAD to the observed SCAFs of 6%. The results highlight the importance of accurately constraining winter accumulation when aiming to reproduce the evolution of glacier mass balance over the melt season and to better separate accumulation and ablation components. Since our method relies on remotely sensed observations and avoids the need for in situ measurements, we conclude that it holds potential for regional-scale glacier monitoring.

Forests play a crucial role in the Earth’s system processes and provide a suite of social and economic ecosystem services, but are significantly impacted by human activities, leading to a pronounced disruption of the equilibrium within ecosystems. Advancing forest monitoring worldwide offers advantages in mitigating human impacts and enhancing our comprehension of forest composition, alongside the effects of climate change. While statistical modeling has traditionally found applications in forest biology, recent strides in machine learning and computer vision have reached important milestones using remote sensing data, such as tree species identification, tree crown segmentation, and forest biomass assessments. For this, the significance of open-access data remains essential in enhancing such data-driven algorithms and methodologies. Here, we provide a comprehensive and extensive overview of 86 open-access forest datasets across spatial scales, encompassing inventories, ground-based, aerial-based, satellite-based recordings, and country or world maps. These datasets are grouped in OpenForest, a dynamic catalog open to contributions that strives to reference all available open-access forest datasets. Moreover, in the context of these datasets, we aim to inspire research in machine learning applied to forest biology by establishing connections between contemporary topics, perspectives, and challenges inherent in both domains. We hope to encourage collaborations among scientists, fostering the sharing and exploration of diverse datasets through the application of machine learning methods for large-scale forest monitoring. OpenForest is available at the following url: https://github.com/RolnickLab/OpenForest.

The Eridu region in southern Mesopotamia was occupied from the sixth until the early first millennium BC, and its archaeological landscape remains well preserved. The present study has identified and mapped a vast, intensive, well-developed network of artificial irrigation canals in this region.

As mid-southern U.S. rice producers continue to adopt furrow-irrigated rice production practices, supplementary management efforts will be vital in combating Palmer amaranth due to the extended germination period provided by the lack of a continual flood. Previous research has revealed the ability of cover crops to suppress Palmer amaranth emergence in corn, cotton, and soybean production systems; however, research on cover crop weed control efficacy in rice production is scarce. Therefore, trials were initiated in Arkansas in 2022 and 2023 to evaluate the effect of cover crops across five site-years on rice emergence, groundcover, grain yield, and total Palmer amaranth emergence. The cover crops evaluated were cereal rye, winter wheat, Austrian winterpea, and hairy vetch. Cover crop biomass accumulation varied by site-year, ranging from 430 to 3,440 kg ha−1, with cereal rye generally being the most consistent producer of high-quantity biomass across site-years. Rice growth and development were generally unaffected by cover crop establishment; however, all cover crops reduced rice emergence by up to 30% in one site-year. Rice groundcover was reduced by 13% from cereal rye in one site-year 2 wk before heading but cover crops did not affect rough rice grain yield in any of the site-years. Palmer amaranth emergence was reduced by 19% and 35% with cereal rye relative to the absence of a cover crop when rice was planted in April in Marianna, and May in Fayetteville, respectively. In most trials, Palmer amaranth emergence was not reduced by a cereal cover crop. In most instances, legume cover crops resulted in less Palmer amaranth emergence than without a cover crop. Based on these results, legume cover crops appear to provide some suppression of Palmer amaranth emergence in furrow-irrigated rice while having a minimal effect on rice establishment and yield.

Passive microwave measurements of Arctic sea ice have been conducted over the last 50 years from space and during airborne, ship- and ground-based measurement campaigns. The different radiometric signatures of distinct surface types have led to satellite retrievals of, e.g., sea-ice concentration. In contrast, ground-based upward-viewing radiometers measure radiation emitted from the atmosphere and are used to retrieve atmospheric variables. Here, we present results from a ship-based radiometer setup with a mirror construction, which allows us to switch between atmospheric and surface measurements flexibly. This way, in summer 2022, surface observations in the Arctic marginal sea-ice zone could be performed from the research vessel Polarstern by two radiometers covering the frequency range from 22 to 243 GHz. At low frequencies, the brightness temperatures show clear signatures of different surface conditions. We estimate emissivities at 53∘ zenith angle from infrared-based skin temperatures. Predominantly vertically polarized 22–31 GHz emissivities are between 0.51 and 0.55 for open ocean and around 0.95 for sea ice. Predominantly horizontally polarized 243 GHz ocean emissivities are around 0.78 and ice surfaces exhibit a large variability from 0.67 to 0.82. Our results can improve the characterization of surface emissions in satellite retrieval algorithms.

Extreme weather events caused by climate change, such as drought and heavy rainfall, will further increase in Central Europe in the near future. Resilient crop production requires in-depth knowledge of soil moisture (SM), its spatial and temporal variability and the dynamics of agriculturally used land. In the current study, different SM estimation methods, including measurement and simulation-based methods, were evaluated over a 17-ha experimental arable crop field with respect to their abilities to capture the spatial and temporal SM dynamics of within-field areas and their related uncertainty and spatial representativeness. The high-spatial resolution in-situ topsoil moisture measurements (50 m grid) were compared with the estimated SM from satellite-based remote sensing (S1ASCAT) and the simulated SM from three different crop water balance models (Agricultural Risk Information System [ARIS], AquaCrop and DSSAT). The evaluation revealed that the spatial variability in the experimental field obtained from the reference could not be captured by the alternative methods investigated because of the limitations of the grid size-related soil map information. Nevertheless, the analysis revealed a very good temporal correlation of SM dynamics with the field area average across all approaches, with AquaCrop and ARIS at a soil depth of 0–10 cm and S1ASCAT soil–water index 05 achieving a R2 and a Kling–Gupta efficiency >0.80. These results indicate the added value of complementary methods for estimating SM to reduce spatial and temporal uncertainties in the estimated topsoil water content.

Currently, methods for mapping agricultural crops have been predominantly developed for a number of the most important and popular crops. These methods are often based on remote sensing data, scarce information about the location and boundaries of fields of a particular crop, and involve analyzing phenological changes throughout the growing season by utilizing vegetation indices, e.g., the normalized difference vegetation index. However, this approach encounters challenges when attempting to distinguish fields with different crops growing in the same area or crops that share similar phenology. This complicates the reliable identification of the target crops based solely on vegetation index patterns. This research paper aims to investigate the potential of advanced techniques for crop mapping using satellite data and qualitative information. These advanced approaches involve interpreting features in satellite images in conjunction with cartographic, statistical, and climate data. The study focuses on data collection and mapping of three specific crops: lavender, almond, and barley, and relies on various sources of information for crop detection, including satellite image characteristics, regional statistical data detailing crop areas, and phenological information, such as flowering dates and the end of the growing season in specific regions. As an example, the study attempts to visually identify lavender fields in Bulgaria and almond orchards in the USA. We test several state-of-the-art methods for semantic segmentation (U-Net, UNet++, ResUnet). The best result was achieved by a ResUnet model (96.4%). Furthermore, the paper explores how vegetation indices can be leveraged to enhance the precision of crop identification, showcasing their advanced capabilities for this task.

Comprehensive housing stock information is crucial for informing the development of climate resilience strategies aiming to reduce the adverse impacts of extreme climate hazards in high-risk regions like the Caribbean. In this study, we propose an end-to-end workflow for rapidly generating critical baseline exposure data using very high-resolution drone imagery and deep learning techniques. Specifically, our work leverages the segment anything model (SAM) and convolutional neural networks (CNNs) to automate the generation of building footprints and roof classification maps. We evaluate the cross-country generalizability of the CNN models to determine how well models trained in one geographical context can be adapted to another. Finally, we discuss our initiatives for training and upskilling government staff, community mappers, and disaster responders in the use of geospatial technologies. Our work emphasizes the importance of local capacity building in the adoption of AI and Earth Observation for climate resilience in the Caribbean.

Glaciers play a crucial role in the Asian Water Tower, underscoring the necessity of accurately assessing their mass balance and ice volume to evaluate their significance as sustainable freshwater resources. In this study, we analyzed ground-penetrating radar (GPR) measurements from a 2020 survey of the Xiao Dongkemadi Glacier (XDG) to determine ice thickness, and we extended the glacier’s volume-change record to 2020 by employing multi-source remote-sensing data. Our findings show that the GPR-derived mean ice thickness of XDG in 2020 was 54.78 ± 3.69 m, corresponding to an ice volume of 0.0811 ± 0.0056 km3. From 1969 to 2020, the geodetic mass balance was −0.19 ± 0.02 m w.e. a−1, and the glacier experienced area and ice volume losses of 16.38 ± 4.66% and 31.01 ± 4.59%, respectively. The long-term mass-balance reconstruction reveals weak fluctuations occurred from 1967 to 1993 and that overall mass losses have occurred since 1994. This ongoing shrinkage and ice loss are mainly associated with the temperature increases in the warm season since the 1960s. If the climate trend across the central Tibetan Plateau follows to the SSP585 scenario, then XDG is at risk of disappearing by the end of the century.

Tropical cyclones can significantly impact mangrove forests, with some recovering rapidly, whilst others may change permanently. Inconsistent approaches to quantifying these impacts limit the capacity to identify patterns of damage and recovery across landscapes and cyclone categories. Understanding these patterns is critical as the changing frequency and intensity of cyclones and compounding effects of climate change, particularly sea-level rise, threaten mangroves and their ecosystem services. Improvements in Earth observation data, particularly satellite-based sensors and datacube environments, have enhanced capacity to classify time-series data and advanced landscape monitoring. Using the Landsat archive within Digital Earth Australia to monitor annual changes in canopy cover and extent, this study aims to quantify and classify immediate and long-term impacts of category 3–5 cyclones for mangroves in Australia. Closed canopy mangrove forests experienced the greatest immediate impact (loss of canopy cover). Most immediate impacts were minor, implying limited immediate mortality. Impacts varied spatially, reflecting proximity to exposed coastlines, cyclone track and forest structure (height, density, condition and species). Recovery was evident across all cyclones, although some areas exhibited permanent damage. Understanding the impacts and characteristics of vulnerable and resilient forests is crucial for managers tasked with protecting mangroves and their services as the climate changes.

Water recreation is valuable to people, and its value can be affected by changes in water quality. This paper presents the results of a revealed preference survey to elicit coastal New England, USA, residents’ values for water recreation and water quality. We combined the survey responses with a comprehensive data set of coastal attributes, including in-water and remotely sensed water quality metrics. Using a travel cost model framework, we found water clarity and the bacterial conditions of coastal waters to be practical water quality inputs to economic analysis, available at appropriate scales, and meaningful to people and their behavior. Changes in clarity and bacterial conditions affected trip values, with a $4.5 change for a meter in clarity in Secchi depth and $0.08 for a one-unit bacteria change in colony-forming units per 100 ml. We demonstrate the large potential value of improving water quality through welfare analysis scenarios for Narragansett Bay, Rhode Island, and Cape Cod, Massachusetts, USA. The paper discusses lessons for improving the policy relevance and applicability of water quality valuation studies through improved water quality data collection, combined with the application of scalable analysis tools for valuation.

The behaviour of mountain glaciers on decadal time scales is a useful indicator for assessing climate change. Although less monitored and studied than the ice sheet, local glaciers and ice caps along the coast of Greenland are substantial contributors to meltwater runoff and sea level rise. This study analyses the cumulative area, ice mass and Equilibrium Line Altitude (ELA) change that occurred on 4100 glaciers and ice caps in West Greenland from 1985 to approximately 2020, using remotely sensed data and including glaciers smaller than 1 km2 in the calculations. The glaciers involved in the study decreased in area by 1774 ± 229 km2 which corresponds to almost −15%. Their surface elevation decreased on average by 20.6 ± 3.9 m, corresponding to a rate of −0.5 ± 0.1 m w.e. a−1. The ELA shows a median regional rise of 150 m with marked local variability and higher median rise in the northern part of the study area. Strong regional gradients in ELA of individual glaciers are found, both towards the ice sheet and in areas where local orography affects precipitation. The observed high spatial variability of changes suggests that more monitoring on sub-regional level is needed.

Information related to the climate, sowing time, harvest, and crop development is essential for defining appropriate strategies for agricultural activities, which helps both producers and responsible bodies. Paraná, the second largest soybean producer in Brazil, has high climatic variability, which greatly influences planting, harvesting, and crop productivity periods. Therefore, the objective of this study was to regionalize the state of Paraná, considering decennial metrics associated with climate variables and the enhanced vegetation index (EVI) during the soybean cycle. Individual and global analyses of these metrics were conducted performed using multivariate techniques. These analyses were carried out in agricultural scenarios with low, medium, and high precipitation, corresponding to harvest years 2011/2012, 2013/2014, and 2015/2016, respectively. The results obtained from the scores of the retained factors and the cluster analysis were the profile of the groups, with Group 1 presenting more favourable climatic and agronomic conditions for the development of soybean crops for the three harvest years. The opposite occurred for Groups 2 (2011/2012 and 2013/2014) and Group 3 (2015/2016). During the soybean reproductive phases (R2 – R5), precipitation values were inadequate, especially for Group 2 (2011/2012 and 2013/2014) with high water deficit, resulting in a drop in soybean productivity. The climatic and agronomic regionalization of Paraná made it possible to identify the regions most suitable for growing soybeans, the effect of climatic conditions on phenological stages, and the variability of soybean productivity in the three harvest years.

Icebergs are part of the glacial mass balance and they interact with the ocean and with sea ice. Optical satellite remote sensing is often used to retrieve the above-waterline area of icebergs. However, varying solar angles introduce an error to the iceberg area retrieval that had not been quantified. Herein, we approximate the iceberg area error for top-of-atmosphere Sentinel-2 near-infrared data at a range of solar zenith angles. First, we calibrate an iceberg threshold at a $56^\circ$ solar zenith angle with reference to higher resolution airborne imagery at Storfjorden, Svalbard. A reflectance threshold of 0.12 yields the lowest relative error of 0.19% ± 15.74% and the lowest interquartile spread. Second, we apply the 0.12 reflectance threshold to Sentinel-2 data at 14 solar zenith angles between $45^\circ$ and $81^\circ$ in the Kangerlussuaq Fjord, south-east Greenland. Here we quantify the error variation with the solar zenith angle for a consistent set of large icebergs. The error variation is then standardized to the error obtained in Svalbard. Up to a solar zenith angle of $65^\circ$, the mean standardized iceberg area error remains between 5.9% and −5.67%. Above $65^\circ$, iceberg areas are underestimated and inconsistent, caused by a segregation into shadows and sun-facing slopes.

Glacier fragmentation involves the detachment of tributary glaciers from the main glacier trunk and their subsequent fragmentation into smaller units. This reconfiguration, in turn, can lead to a redistribution of stresses and strain rates affecting the dynamics of the glacier. In our study, we examined changes in the frontal position and surface velocity of Bertacchi and Upsala Glaciers using Sentinel-1 derived velocity fields and orthoimages, covering the period between January 2015 and January 2023. Comparison of these results with bed topography and ice thickness datasets indicates that the Bertacchi tributary glacier acted as a strong lateral pinning point for the main flow unit from 2015 to 2018. This slowed its retreat rate to −6 ± 2.5 ma−1 despite the high surface velocity (1825 ± 11 ma−1) and buoyancy conditions. However, the loss of this pinning point in early 2019 led to accelerated retreat rates (−325 ± 2.5 ma−1) of the western tongue of Upsala Glacier, even though it retreated over a shallow bed and the surface velocity was 45% lower than previous. This retreat was synchronous with the advance of Bertacchi terminus (15 ± 2.5 ma−1), suggesting a reduction in the resistive stresses experienced by this glacier following unpinning.

The Battle of al-Qadisiyyah (c. AD 637/8) was a crucial victory by the Arab Muslims over the forces of the Sasanian Empire during the early Islamic conquests. Analysis of satellite imagery of south-west Iraq has now revealed the likely location of this important historic battle.